import torch
from torch import Tensor
import torch.nn as nn


def channel_shuffle(x, groups):
    batch_size, num_channels, height, width = x.size()
    channels_per_group = num_channels // groups
    x = x.view(batch_size, groups, channels_per_group, height, width)
    x = x.transpose(1, 2).contiguous()
    x = x.view(batch_size, -1, height, width)
    return x


def depthwise_conv(input_c, output_c, kernel_s, stride=1, padding=0, bias=False):
    return nn.Conv2d(in_channels=input_c, out_channels=output_c, kernel_size=kernel_s,
                     stride=stride, padding=padding, bias=bias, groups=input_c)


class InvertedResidual(nn.Module):
    def __init__(self, input_c, output_c, stride):
        super(InvertedResidual, self).__init__()
        assert stride in [1, 2]
        self.stride = stride
        assert output_c % 2 == 0
        branch_features = output_c // 2
        assert (self.stride != 1) or (input_c == branch_features << 1)
        if self.stride == 2:
            self.branch1 = nn.Sequential(
                depthwise_conv(input_c, input_c, kernel_s=3, stride=self.stride, padding=1),
                nn.BatchNorm2d(input_c),
                nn.Conv2d(input_c, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
                nn.BatchNorm2d(branch_features),
                nn.ReLU(inplace=True)
            )
        else:
            self.branch1 = nn.Sequential()
        self.branch2 = nn.Sequential(
            nn.Conv2d(input_c if self.stride > 1 else branch_features, branch_features, kernel_size=1,
                      stride=1, padding=0, bias=False),
            nn.BatchNorm2d(branch_features),
            nn.ReLU(inplace=True),
            depthwise_conv(branch_features, branch_features, kernel_s=3, stride=self.stride, padding=1),
            nn.BatchNorm2d(branch_features),
            nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(branch_features),
            nn.ReLU(inplace=True)
        )

    def forward(self, x):
        if self.stride == 1:
            x1, x2 = x.chunk(2, dim=1)
            out = torch.cat((x1, self.branch2(x2)), dim=1)
        else:
            out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)
        out = channel_shuffle(out, 2)
        return out


class ShuffleNetV2(nn.Module):
    def __init__(self, num_classes=5, inverted_residual=InvertedResidual):
        super(ShuffleNetV2, self).__init__()
        stages_repeats = [4, 8, 4]
        self.stage_out_channels = [24, 116, 232, 464, 1024]
        # input RGB image
        input_channels = 3
        output_channels = self.stage_out_channels[0]
        self.conv1 = nn.Sequential(
            nn.Conv2d(input_channels, output_channels, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(output_channels),
            nn.ReLU(inplace=True)
        )
        input_channels = output_channels
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        # Static annotations for mypy
        self.stage2: nn.Sequential
        self.stage3: nn.Sequential
        self.stage4: nn.Sequential
        stage_names = ["stage{}".format(i) for i in [2, 3, 4]]
        for name, repeats, output_channels in zip(stage_names, stages_repeats,
                                                  self.stage_out_channels[1:]):
            seq = [inverted_residual(input_channels, output_channels, 2)]
            for i in range(repeats - 1):
                seq.append(inverted_residual(output_channels, output_channels, 1))
            setattr(self, name, nn.Sequential(*seq))
            input_channels = output_channels
        output_channels = self.stage_out_channels[-1]
        self.conv5 = nn.Sequential(
            nn.Conv2d(input_channels, output_channels, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(output_channels),
            nn.ReLU(inplace=True)
        )
        self.fc = nn.Linear(output_channels, num_classes)

    def forward(self, x):
        x = self.conv1(x)
        x = self.maxpool(x)
        x = self.stage2(x)
        x = self.stage3(x)
        x = self.stage4(x)
        x = self.conv5(x)
        x = x.mean([2, 3])
        x = self.fc(x)
        return x
